OpenRaiser/Intern-Atlas huggingface dataset:
GraspLook: a VR-based Telemanipulation System with R-CNN-driven Augmentation of Virtual Environment
Venue: arXiv cs.RO 2021
Year: 2021

The teleoperation of robotic systems in medical applications requires stable and convenient visual feedback for the operator. The most accessible approach to delivering visual information from the remote area is using cameras to transmit a video stream from the environment. However, such systems are sensitive to the camera resolution, limited viewpoints, and cluttered environment bringing additional mental demands to the human operator. The paper proposes a novel system of teleoperation based on an augmented virtual environment (VE). The region-based convolutional neural network (R-CNN) is applied to detect the laboratory instrument and estimate its position in the remote environment to display further its digital twin in the VE, which is necessary for dexterous telemanipulation. The experimental results revealed that the developed system allows users to operate the robot smoother, which leads to a decrease in task execution time when manipulating test tubes. In addition, the participants evaluated the developed system as less mentally demanding (by 11%) and requiring less effort (by 16%) to accomplish the task than the camera-based teleoperation approach and highly assessed their performance in the augmented VE. The proposed technology can be potentially applied for conducting laboratory tests in remote areas when operating with infectious and poisonous reagents.
Towards Blockchain-enabled Open Architectures for Scalable Digital Asset Platforms
Venue: arXiv cs.CR 2021
Year: 2021

Today there is considerable interest in deploying blockchains and decentralized ledger technology as a means to address the deficiencies of current financial and digital asset infrastructures. The focal point of attention in many projects on digital asset and cryptocurrency is centered around blockchain systems and smart contracts. Many projects seek to make the blockchain as the centerpiece of the new decentralized world of finance. However, several roadblocks and challenges currently face this predominant blockchain-centric view. In this paper we argue that the proper and correct perspective on decentralized economy should be one that is asset-centric, where the goal should be the consistent lifecycle management of assets in the real-world with their digital representation on the blockchain. We introduce the notion of the digital twin to capture the relationship between a real-world asset and its on-chain representation. A digital twin container is utilized to permit off-chain state persistence and on-chain state traceability, where the container can be deployed on the blockchain as well as on traditional application servers. The digital twin container becomes the bridge between legacy infrastructures and the newly emergent blockchain infrastructures, permitting legacy systems to interoperate consistently with blockchain systems. We believe this asset-centric view to be the correct evolutionary direction for the nascent field of blockchains and decentralized ledger technology.
Towards a Formalisation of Justification and Justifiability
Venue: arXiv cs.LO 2021
Year: 2021

We introduce the logic QKSD which is a normal multi-modal logic over finitely many modalities that additionally supports bounded quantification of modalities. An important feature of this logic is that it allows to quantify over the information components of systems and, hence, can be used to derive justifications. We compare the proposed logic with Artemov's justification logic and also report on a prototypical implementation of a satisfiability solver of this logic and show some examples.
Towards Organic 6G Networks: Virtualization and Live Migration of Core Network Functions
Venue: arXiv cs.NI 2021
Year: 2021

In the context of Industry 4.0, more and more mobile use cases are appearing on industrial factory floors. These use cases place high demands on various quantitative requirements, such as latency, availability, and more. In addition, qualitative requirements such as flexibility are arising. Since virtualization technology is a key enabler for the flexibility that is required by novel use cases and on the way to organic networking as it is addressed by 6G, we investigate container virtualization technology in this paper. We focus on container technology since OS-level virtualization has multiple benefits compared to hardware virtualization, such as VMs. Thus, we discuss several aspects of container based virtualization, e.g. selection of suitable network drivers and orchestration tools, with respect to most important 5GC functions. In addition, the functions have different quantitative or qualitative requirements depending on whether they are stateless or stateful, and whether the specific function is located at either the control or user plane. Therefore, we also analyze the aforementioned live migration concepts for the 5GC functions and evaluate them based on well-defined metrics, such as migration time and process downtime.
An approach to optimize study programs using Discrete Event Simulation
Venue: arXiv cs.CY 2021
Year: 2021

Creating a study program for optimal academic completion is a complex assignment. Especially programs in the science, technology, engineering, and mathematics field are known for extended completion time as well as high drop-out rates throughout the years. Drop-outs are caused by various reasons and can not be directly generalized. This leads to unnecessary costs for the students and the university. Reasons for dropping out of university could be students failing classes too often or poorly designed study programs causing a loss of interest in a subject. Besides dropping out of university, students are often having trouble with specific classes. This results in postponing certain classes, which causes a bottleneck in the overall progression and delays graduation. To achieve a better understanding of a general student's progression as well as finding mentioned bottlenecks in an average academic progression a discrete event simulation was created. The insights gained by the simulation shall furthermore be used by Technische Hochschule Köln to analyze mentioned problems, find solutions and create a healthier environment for optimal academic progression.
Spread2RML: Constructing Knowledge Graphs by Predicting RML Mappings on Messy Spreadsheets
Venue: arXiv cs.DB 2021
Year: 2021

The RDF Mapping Language (RML) allows to map semi-structured data to RDF knowledge graphs. Besides CSV, JSON and XML, this also includes the mapping of spreadsheet tables. Since spreadsheets have a complex data model and can become rather messy, their mapping creation tends to be very time consuming. In order to reduce such efforts, this paper presents Spread2RML which predicts RML mappings on messy spreadsheets. This is done with an extensible set of RML object map templates which are applied for each column based on heuristics. In our evaluation, three datasets are used ranging from very messy synthetic data to spreadsheets from data.gov which are less messy. We obtained first promising results especially with regard to our approach being fully automatic and dealing with rather messy data.
On Slowly-varying Non-stationary Bandits
Venue: arXiv cs.LG 2021
Year: 2021

We consider minimisation of dynamic regret in non-stationary bandits with a slowly varying property. Namely, we assume that arms' rewards are stochastic and independent over time, but that the absolute difference between the expected rewards of any arm at any two consecutive time-steps is at most a drift limit $δ> 0$. For this setting that has not received enough attention in the past, we give a new algorithm which extends naturally the well-known Successive Elimination algorithm to the non-stationary bandit setting. We establish the first instance-dependent regret upper bound for slowly varying non-stationary bandits. The analysis in turn relies on a novel characterization of the instance as a detectable gap profile that depends on the expected arm reward differences. We also provide the first minimax regret lower bound for this problem, enabling us to show that our algorithm is essentially minimax optimal. Also, this lower bound we obtain matches that of the more general total variation-budgeted bandits problem, establishing that the seemingly easier former problem is at least as hard as the more general latter problem in the minimax sense. We complement our theoretical results with experimental illustrations.
Accelerating Compact Fractals with Tensor Core GPUs
Venue: arXiv cs.DC 2021
Year: 2021

This work presents a GPU thread mapping approach that allows doing fast parallel stencil-like computations on discrete fractals using their compact representation. The intuition behind is to employ two GPU tensor-core accelerated thread maps, $λ(ω)$ and $ν(ω)$, which act as threadspace-to-dataspace and dataspace-to-threadspace functions, respectively. By combining these maps, threads can access compact space and interact with their neighbors. The cost of the maps is $\mathcal{O}(\log \log(n))$ time, with $n$ being the side of a $n \times n$ embedding for the fractal in its expanded form. The technique works on any fractal that belongs to the Non-overlapping-Bounding-Boxes (NBB) class of discrete fractals, and can be extended to three dimensions as well. Results using an A100 GPU on the Sierpinski Triangle as a case study show up to $\sim11\times$ of speedup and a memory usage reduction of $234\times$ with respect to a Bounding Box approach. These results show that the proposed compact approach can allow the scientific community to tackle larger problems that did not fit in GPU memory before, and run even faster than a bounding box approach.
Fast Multimodal Journey Planning for Three Criteria
Venue: arXiv cs.DS 2021
Year: 2021

We study the journey planning problem for multimodal networks consisting of public transit and a non-schedule-based transfer mode (e.g., walking, bicycle, e-scooter). So far, all efficient algorithms for this problem either restrict usage of the transfer mode or Pareto-optimize only two criteria: arrival time and the number of used public transit trips. However, we show that both limitations must be lifted in order to obtain high-quality solutions. In particular, the time spent using the (unrestricted) transfer mode must be optimized as a third criterion. We present McTB, the first algorithm that optimizes three criteria efficiently by avoiding costly data structures for maintaining Pareto sets. To enable unlimited transfers, we combine it with a three-criteria extension of the ULTRA preprocessing technique. Furthermore, since full Pareto sets become impractically large for more than two criteria, we adapt an approach by Delling et al. to restrict the Pareto set in a methodical manner. Extensive experiments on real-world data show that our algorithms are fast enough for interactive queries even on large country-sized networks. Compared to the state of the art for multicriteria multimodal journey planning, MCR, we achieve a speedup of up to 80.
Blockchain application in simulated environment for Cyber-Physical Systems Security
Venue: arXiv cs.CR 2021
Year: 2021

Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.
Optimization-Based GenQSGD for Federated Edge Learning
Venue: arXiv cs.LG 2021
Year: 2021

Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may have different computation and communication capabilities, and quantized intermediate model updates are sent between the server and workers. First, we present a general quantized parallel mini-batch stochastic gradient descent (SGD) algorithm for FL, namely GenQSGD, which is parameterized by the number of global iterations, the numbers of local iterations at all workers, and the mini-batch size. We also analyze its convergence error for any choice of the algorithm parameters. Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint. The optimization problem is a challenging non-convex problem with non-differentiable constraint functions. We propose an iterative algorithm to obtain a KKT point using advanced optimization techniques. Numerical results demonstrate the significant gains of GenQSGD over existing FL algorithms and reveal the importance of optimally designing FL algorithms.
Generating artificial texts as substitution or complement of training data
Venue: arXiv cs.CL 2021
Year: 2021

The quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised learning tasks naturally arises. In this article, this question is explored under 3 aspects: (i) are artificial data an efficient complement? (ii) can they replace the original data when those are not available or cannot be distributed for confidentiality reasons? (iii) can they improve the explainability of classifiers? Different experiments are carried out on Web-related classification tasks -- namely sentiment analysis on product reviews and Fake News detection -- using artificially generated data by fine-tuned GPT-2 models. The results show that such artificial data can be used in a certain extend but require pre-processing to significantly improve performance. We show that bag-of-word approaches benefit the most from such data augmentation.
Rotation Equivariant Deforestation Segmentation and Driver Classification
Venue: arXiv cs.CV 2021
Year: 2021

Deforestation has become a significant contributing factor to climate change and, due to this, both classifying the drivers and predicting segmentation maps of deforestation has attracted significant interest. In this work, we develop a rotation equivariant convolutional neural network model to predict the drivers and generate segmentation maps of deforestation events from Landsat 8 satellite images. This outperforms previous methods in classifying the drivers and predicting the segmentation map of deforestation, offering a 9% improvement in classification accuracy and a 7% improvement in segmentation map accuracy. In addition, this method predicts stable segmentation maps under rotation of the input image, which ensures that predicted regions of deforestation are not dependent upon the rotational orientation of the satellite.
Diagnosing Errors in Video Relation Detectors
Venue: arXiv cs.CV 2021
Year: 2021

Video relation detection forms a new and challenging problem in computer vision, where subjects and objects need to be localized spatio-temporally and a predicate label needs to be assigned if and only if there is an interaction between the two. Despite recent progress in video relation detection, overall performance is still marginal and it remains unclear what the key factors are towards solving the problem. Following examples set in the object detection and action localization literature, we perform a deep dive into the error diagnosis of current video relation detection approaches. We introduce a diagnostic tool for analyzing the sources of detection errors. Our tool evaluates and compares current approaches beyond the single scalar metric of mean Average Precision by defining different error types specific to video relation detection, used for false positive analyses. Moreover, we examine different factors of influence on the performance in a false negative analysis, including relation length, number of subject/object/predicate instances, and subject/object size. Finally, we present the effect on video relation performance when considering an oracle fix for each error type. On two video relation benchmarks, we show where current approaches excel and fall short, allowing us to pinpoint the most important future directions in the field. The tool is available at \url{https://github.com/shanshuo/DiagnoseVRD}.
Detecting Wandering Behavior of People with Dementia
Venue: arXiv cs.LG 2021
Year: 2021

Wandering is a problematic behavior in people with dementia that can lead to dangerous situations. To alleviate this problem we design an approach for the real-time automatic detection of wandering leading to getting lost. The approach relies on GPS data to determine frequent locations between which movement occurs and a step that transforms GPS data into geohash sequences. Those can be used to find frequent and normal movement patterns in historical data to then be able to determine whether a new on-going sequence is anomalous. We conduct experiments on synthetic data to test the ability of the approach to find frequent locations and to compare it against an alternative, state-of-the-art approach. Our approach is able to identify frequent locations and to obtain good performance (up to AUC = 0.99 for certain parameter settings) outperforming the state-of-the-art approach.
Decomposed Inductive Procedure Learning
Venue: arXiv cs.LG 2021
Year: 2021

Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks. However, the process of training these capabilities often necessitates millions of annotated examples -- far more than humans typically need in order to achieve a passing level of mastery on similar tasks. Thus, while contemporary methods in machine learning can produce agents that exhibit super-human performance, their rate of learning per opportunity in many domains is decidedly lower than human-learning. In this work we formalize a theory of Decomposed Inductive Procedure Learning (DIPL) that outlines how different forms of inductive symbolic learning can be used in combination to build agents that learn educationally relevant tasks such as mathematical, and scientific procedures, at a rate similar to human learners. We motivate the construction of this theory along Marr's concepts of the computational, algorithmic, and implementation levels of cognitive modeling, and outline at the computational-level six learning capacities that must be achieved to accurately model human learning. We demonstrate that agents built along the DIPL theory are amenable to satisfying these capacities, and demonstrate, both empirically and theoretically, that DIPL enables the creation of agents that exhibit human-like learning performance.
Image Quality Assessment using Contrastive Learning
Venue: arXiv cs.CV 2021
Year: 2021

We consider the problem of obtaining image quality representations in a self-supervised manner. We use prediction of distortion type and degree as an auxiliary task to learn features from an unlabeled image dataset containing a mixture of synthetic and realistic distortions. We then train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We refer to the proposed training framework and resulting deep IQA model as the CONTRastive Image QUality Evaluator (CONTRIQUE). During evaluation, the CNN weights are frozen and a linear regressor maps the learned representations to quality scores in a No-Reference (NR) setting. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models, even without any additional fine-tuning of the CNN backbone. The learned representations are highly robust and generalize well across images afflicted by either synthetic or authentic distortions. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets. The implementations used in this paper are available at \url{https://github.com/pavancm/CONTRIQUE}.
Facial Recognition in Collaborative Learning Videos
Venue: arXiv cs.CV 2021
Year: 2021

Face recognition in collaborative learning videos presents many challenges. In collaborative learning videos, students sit around a typical table at different positions to the recording camera, come and go, move around, get partially or fully occluded. Furthermore, the videos tend to be very long, requiring the development of fast and accurate methods. We develop a dynamic system of recognizing participants in collaborative learning systems. We address occlusion and recognition failures by using past information about the face detection history. We address the need for detecting faces from different poses and the need for speed by associating each participant with a collection of prototype faces computed through sampling or K-means clustering. Our results show that the proposed system is proven to be very fast and accurate. We also compare our system against a baseline system that uses InsightFace [2] and the original training video segments. We achieved an average accuracy of 86.2% compared to 70.8% for the baseline system. On average, our recognition rate was 28.1 times faster than the baseline system.
Controlling Smart Propagation Environments: Long-Term versus Short-Term Phase Shift Optimization
Venue: arXiv cs.IT 2021
Year: 2021

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to analyze and compare against analytic results in finite samples. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks.